Staff / Principal Research Engineer, AI Safety Mitigations

Description

Your Impact at LILA

We're building a talent-dense, high-agency AI safety team at Lila that will engage all core teams within the organization (science, model training, lab integration, etc.), to identify and mitigate risks from scientific superintelligence while retaining the core scientific discovery capabilities of the stack. The initial focus of this team will be to build and implement a bespoke safety strategy for Lila, tailored to its specific goals and deployment strategies. This will involve technical safety strategy development, broader ecosystem engagement, safety-focused evaluations, safety systems to mitigate risks, and a research agenda that explores longer-term needs such as oversight of superintelligent scientific systems.

At Lila Sciences, you will develop and test safeguards that reduce societal risks from increasingly capable closed loop scientific AI. This is a high-priority individual contributor role for an engineer who has built safety interventions within the development and deployment of frontier models and wants to apply that experience to scientific models, agents, tools and laboratory-connected workflows.

You will work with domain scientists, risk researchers, evaluators and engineering teams to turn risk findings and policy requirements into effective interventions. The work spans research and implementation: developing approaches, testing their limits and helping integrate reliable safeguards into the systems where they are needed. We are open to different levels of seniority and depth across mitigation approaches.

What You'll Be Building

Translate scientific risk assessments, dual-use policy boundaries and evaluation findings into concrete mitigation designs and testable requirements.

Develop and implement safeguards suited to the problem, potentially spanning model training, classifiers, monitoring, access controls and runtime interventions.

Build experiments that measure risk reduction, robustness and effects on legitimate scientific use, alongside practical performance constraints.

Use adversarial findings and observed failures to improve safeguards, working closely with automated-safety and scientific evaluation researchers.

Work directly with model-training, post-training, inference and platform teams to integrate mitigations into model development, release evaluation and large-scale serving systems, with appropriate instrumentation and reproducible testing.

Investigate regressions, distribution shifts and emerging misuse patterns, and use the evidence to guide further development.

Requirements

• Hands-on experience developing and validating safety interventions for frontier models within training, post-training or large-scale inference systems, with contributions to model development or release cycles.
• Depth in at least one relevant area, such as safety post-training, model-level classifiers, safeguards integrated into inference, or abuse monitoring and enforcement across a model-serving platform.
• Experience working directly with model-development or safeguards teams, using access appropriate to the intervention—such as training pipelines, model internals, inference infrastructure or systematic evaluation access—to investigate failures and implement improvements.
• Strong research-engineering skills and the ability to move from an ambiguous problem to working, maintainable implementations.
• Sound experimental judgment, including an ability to assess robustness, interpret imperfect metrics and reason about competing objectives.
• The ability to collaborate across research and engineering teams, explain technical trade-offs and take ownership of complex individual contributions.
• Bonus Points For
• Experience with scientific models, scientific software or laboratory-connected systems.
• Safety work involving tool-using agents, multi-step workflows or high-consequence misuse.
• Experience connecting expert-generated evidence and policy requirements to training data, evaluation criteria or operational controls.

Conditions

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.

U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.

International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Expected Base Salary Range

$224,000 - $336,000 USD

About LILA

Lila Sciences is building Scientific Superintelligence to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.

LILA combines advanced AI models with proprietary AI Science Factory instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.

Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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